Conference Agenda

Sea ice is a highly sensitive indicator of past and present climate change. The demand for getting comprehensive, continuous, and reliable sea ice information from multi-source satellite data is growing as a result of climate change and its impact on environment and regional weather conditions, and on human activities such as operations in ice-covered ocean regions. This paper provides an overview of the Dragon 5 project dealing with synergistic monitoring of sea ice in the Arctic by multi-source remote sensing data.

For sea ice classification, the multi-frequency polarimetric backscatter behavior of sea ice during the melt period was investigated. Multi-frequency (L-, S-, C-, X- and Ku-band) airborne SAR scenes were recorded in the Bohai Sea with air temperatures varying around 0℃. In this work, we quantified the redundancy and relevance of polarimetric features for identifying ice types during the melting period, and assess the discrimination ability of melting sea ice types at the different radar frequencies. Considering the needs of operational Ice Services responsible for producing sea ice maps, another study dealt with a comparison of ice type separation in satellite C- and L-band SAR images as stand-alone and in combination. Since L- and C-band SAR systems have to be operated from different satellite platforms, an optimal data acquisition strategy has also to be developed. For sea ice thickness, we analyzed the feasibility of retrieving Arctic sea ice thickness from the Chinese HY-2B Ku-band radar altimeter. To this end, we used the HY-2B radar altimeter to retrieve the Arctic radar freeboard and sea ice thickness, and compared the results with the co-incident CryoSat-2 products by AWI. By comparing with the OIB and IceSAT-2 data, we found that the deviations in radar freeboard and sea ice thickness between HY-2B and CS-2 over multiyear ice are larger than those over first-year ice. For iceberg detection by SAR data, the variations of signature contrast between icebergs and sea ice dependent on ice conditions and radar parameters was investigated. We found that the intensity contrast depends on the radar frequency, the incidence angle and the sea ice surface characteristics. The latter study will be presented by our young investigators.

Sea ice drift and thickness retrieval methods that are specifically designed for the FY-3D radiometer were proposed. For sea ice drift in the Arctic we used a continuous maximum correlation (CMCC) approach. To address the challenge of retrieving Arctic sea ice thickness, a FY-3D specific method was developed that relies on different parameters derived from the brightness temperature data (i.e. polarization ratio and gradient ratio). Besides estimating sea ice thickness with radiometer data we also investigated detection of thin ice (<20 cm) in the Arctic using AMSR2 and FY-3C radiometer data. The thin ice detection is based on the classification of the 36 GHz polarization ratio and H-polarization 89-36 GHz gradient ratio (GR) with linear discrimination analysis, and thick ice restoration with GR3610H. An integral part of the thin ice detection is the atmospheric correction of the brightness temperature data, following an EUMETSAT OSI SAF correction scheme. The thin ice detection algorithm was developed using MODIS ice thickness charts over the Barents and Kara Seas. The AMSR2 and FY-3C daily thin ice charts are calculated for one winter season, and their statistical similarities and differences are investigated. They are also compared against the SMOS ice thickness data. The AMSR2 and MWRI daily thin ice charts are targeted to be used together with SAR imagery for sea ice classification.

The contribution of meltwater from the snowpack and glaciers in High Mountain Asia (HMA) is rather well documented, as are changes in glacier extent and volume. Less explored are the overall dynamics of the high mountain water cycle, and the interactions of snow and ice dynamics with those of vegetation to shape HMA catchments response to weather and climate and their water yield .
This is the goal of our project and we made progress on several aspects, linking progresses in remote sensing and advanced land surface modelling to advance simulations of HMA water cycle. Inter- and intra-annual elevation changes of glaciers in the HMA region in 2003–2020 were studied using Ice, Cloud and land Elevation Satellite (ICESat) data and Shuttle Radar Terrain Mission (SRTM) digital elevation model (DEM) data. The inter-annual change of glacier elevation in 2003–2020 had large spatial heterogeneity. Glacier elevation reduction mainly occurred in the marginal region of the HMA with the maximum decline in the Nyainqentanglha region, while glacier elevation increased in the West Kunlun of inner HMA regions in 2003–2020. The intra-annual change of HMA glacier elevation in 2019 and 2020 showed a clear spatiotemporal heterogeneity, and the glacier thickening period was gradually delayed from the marginal area to the inner area of the HMA.
The inter- and interannual variability in snow cover during the period 2000 – 2020 was studied in the Tarim Basin to understand changes in glacier extent in relation with snow accumulation. We evaluated how observed trends in glacier area related to snow cover area in five subregions in the Tarim Basin. We studied the temporal variability of snow cover on different temporal scales. The analysis of the monthly snow cover showed that permanent snow can be reliably delineated in the months from July to September. The analysis of the cross- correlation functions of glacier and snow cover areas showed that the glacier area responds to temperature, precipitation and snow cover within the same year.
Glacier surface albedo is one of the most important parameters to determine the net shortwave radiation and therefore affect glacier energy and mass balance, which in turn affect glacier surface flow, especially its spatial and temporal variability. The results show that the glacier albedo declined over the entire HMA, but with distinct spatial and seasonal differences. In the westerly-dominated regions, glacier albedo decreased slightly and even increased in the Hindu Kush and West Himalaya, while in the monsoon-dominated and transition regions, it showed a large decrease with the fastest change in the Inner Tibetan Plateau.
Patterns of glacier surface velocity and its seasonal and interannual variability in the temperate glaciers of the Parlung Zangbo Basin (PZB) are still uncertain. On the basis of satellite images acquired from 2013 to 2020, we produced a map of time-averaged glacier surface velocity and examined four typical glaciers (Yanong, Parlung No.4, Xueyougu, and Azha) in the PZB. Next, we explored the driving factors of surface velocity and of its variability. The results show that the glacier centerline velocity increased slightly in 2017–2020. The accumulated ice mass could have caused seasonal velocity changes in response to mass imbalance during 2017–2020. There was a clear winter-spring speedup of 40% in the upper glacier region, while a summer speedup occurred at the glacier tongue.
The generation of water in HMA headwaters and their downstream flow is not fully understood, as interactions in the mountain water cycle between the hydrosphere and biosphere remain elusive. Understanding how blue meltwater from snow-pack and glaciers contribute to total water vapour flux from vegetation, both is high elevation catchments and further downstream is a pressing and challenging research question. We applied a state-of-the-art land surface model (Tethys & Chloris) to the glacierized Langtang catchment in the Nepalese Himalayas to demonstrate the advantage of using high resolution earth observation data on e.g. glacier thinning and surface flow, glacier albedo and snow cover to constrain the meteorological uncertainty and validate our model results.